{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 掷骰子\n",
    "生成数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4716\n",
      "1.7033477155296273\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "random_data = np.random.randint(1, 7, 10000)\n",
    "print(random_data.mean())   # 平均数\n",
    "print(random_data.std())    # 标准差\n",
    "\n",
    "plt.hist(random_data, bins=50, density=False, color='red')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 6, 6, 1, 5, 2, 4, 4, 6, 5]\n",
      "[2 6 6 1 5 2 4 4 6 5]\n",
      "4.1\n",
      "1.7578395831246945\n"
     ]
    }
   ],
   "source": [
    "#随机抽取10个数字\n",
    "sample1 = []\n",
    "for i in range(0, 10):\n",
    "    sample1.append(random_data[int(np.random.random() * len(random_data))])\n",
    "print(sample1)\n",
    "samples = np.array(sample1)\n",
    "print(samples)\n",
    "print(samples.mean())\n",
    "print(samples.std())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1000组，每组50个数字\n",
    "random_data = np.random.randint(1, 7, 100000)\n",
    "samples = []\n",
    "samples_mean = []\n",
    "samples_std = []\n",
    "\n",
    "for i in range(0, 10000):\n",
    "    sample = []\n",
    "    for j in range(0, 100):\n",
    "        sample.append(random_data[int(np.random.random() * len(random_data))])\n",
    "    samples_np = np.array(sample)\n",
    "    samples_mean.append(samples_np.mean())\n",
    "    samples_std.append(samples_np.std())\n",
    "    samples.append(samples_np)\n",
    "\n",
    "samples_mean_np = np.array(samples_mean)\n",
    "samples_std_np = np.array(samples_std)\n",
    "\n",
    "plt.hist(samples_mean_np, bins=50, density=False, color='red')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}